Word2vec is classified as a self-supervised machine learning model. Therefore, the number of dimensions can be selected by comparing the value of the loss function on the validation dataset. An alternative approach to dimensionality selection involves using information criteria such as the Akaike Information Criterion (AIC) [1]}, Bayesian Information Criterion (BIC) [2]}, and Minimum Description Length (MDL) [3]}. Compared to the cross-validation method, these information criteria do not require a hold-out validation dataset, which prevents wastage of our precious data.
